Rune Sætre | Norwegian University of Science and Technology (original) (raw)

Papers by Rune Sætre

Natural language processing modules such as part-of-speech taggers, named-entity recognizers and ... more Natural language processing modules such as part-of-speech taggers, named-entity recognizers and syntactic parsers are commonly evaluated in isolation, under the assumption that artificial evaluation metrics for individual parts are predictive of practical performance of more complex language technology systems that perform practical tasks. Although this is an important issue in the design and engineering of systems that use natural language input, it is often unclear how the accuracy of an end-user application is affected by parameters that affect individual NLP modules. We explore this issue in the context of a specific task by examining the relationship between the accuracy of a syntactic parser and the overall performance of an information extraction system for biomedical text that includes the parser as one of its components. We present an empirical investigation of the relationship between factors that affect the accuracy of syntactic analysis, and how the difference in parse ...

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2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011

BMC Bioinformatics, 2011

Background: Bio-molecular event extraction from literature is recognized as an important task of ... more Background: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.

Proceedings of the Workshop on BioNLP Shared Task - BioNLP '09, 2009

This document describes the methods and results for our participation in the BioNLP'09 Shared Tas... more This document describes the methods and results for our participation in the BioNLP'09 Shared Task #1 on Event Extraction. It also contains some error analysis and a brief discussion of the results. Previous shared tasks in the BioNLP community have focused on extracting gene and protein names, and on finding (direct) protein-protein interactions (PPI). This year's task was slightly different, since the protein names were already manually annotated in the text. The new challenge was to extract biological events involving these given gene and gene products. We modified a publicly available system (AkanePPI) to apply it to this new, but similar, protein interaction task. AkanePPI has previously achieved state-of-the-art performance on all existing public PPI corpora, and only small changes were needed to achieve competitive results on this event extraction task. Our official result was an F-score of 36.9%, which was ranked as number six among submissions from 24 different groups. We later balanced the recall/precision by including more predictions than just the most confident one in ambiguous cases, and this raised the F-score on the test-set to 42.6%. The new Akane program can be used freely for academic purposes.

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 1 - EMNLP '09, 2009

Because of the importance of proteinprotein interaction (PPI) extraction from text, many corpora ... more Because of the importance of proteinprotein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and PPI. Since no single corpus is large enough to saturate a machine learning system, it is necessary to learn from multiple different corpora. In this paper, we propose a solution to this challenge. We designed a rich feature vector, and we applied a support vector machine modified for corpus weighting (SVM-CW) to complete the task of multiple corpora PPI extraction. The rich feature vector, made from multiple useful kernels, is used to express the important information for PPI extraction, and the system with our feature vector was shown to be both faster and more accurate than the original kernelbased system, even when using just a single corpus. SVM-CW learns from one corpus, while using other corpora for support. SVM-CW is simple, but it is more effective than other methods that have been successfully applied to other NLP tasks earlier. With the feature vector and SVM-CW, our system achieved the best performance among all state-of-the-art PPI extraction systems reported so far.

Page 1. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, ... more Page 1. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 22–31, Portland, Oregon, June 19-24, 2011. cO2011 Association for Computational Linguistics Effective Use of Function Words for Rule Generalization ...

… Workshop on EUD4 …, 2011

Intelligent objects and devices are becoming part of the environment where people live. The more ... more Intelligent objects and devices are becoming part of the environment where people live. The more mobile and pervasive computing becomes, the greater opportunity users potentially have to customize the computing activities that take place around them. For some people the availability of devices and services offers possibilities for tailoring things to exactly what they want. For others, however, this represents a problem: how to manage the complexity? It is neither practical nor economical to use professional software developers for individual tailoring. Thus, we have to provide users with easily operable tools for service composition. The goal of this paper is to highlight the main challenges for a meaningful end-user tool support.

Abstract Nowadays, an increasing number of language resources including both corpora and tools fo... more Abstract Nowadays, an increasing number of language resources including both corpora and tools for Text Mining (TM) and Natural Language Processing (NLP) are available. Because most of TM/NLP tasks are composite by nature, the interoperability between tools ...

Background: Extracting Protein-Protein Interactions (PPI) from research papers is a way of transl... more Background: Extracting Protein-Protein Interactions (PPI) from research papers is a way of translating information from English to the language used by the databases that store this information. With recent advances in automatic PPI detection, it is now possible to speed up this process considerably. Syntactic features from different parsers for biomedical English text are readily available, and can be used to improve the performance of such PPI extraction systems. Results: A complete PPI system was built. It uses a deep syntactic parser to capture the semantic meaning of the sentences, and a shallow dependency parser to improve the performance further. Machine learning is used to automatically make rules to extract pairs of interacting proteins from the semantics of the sentences. The results have been evaluated using the AImed corpus, and they are better than earlier published results. The F-score of the current system is 69.5% for cross-validation between pairs that may come from the same abstract, and 52.0% when complete abstracts are hidden until final testing. Automatic 10-fold cross-validation on the entire AImed corpus can be done in less than 45 minutes on a single server. We also present some previously unpublished statistics about the AImed corpus, and a short analysis of the AImed representation language. Conclusions: We present a PPI extraction system, using different syntactic parsers to extract features for SVM with Tree Kernels, in order to automatically create rules to discover protein interactions described in the molecular biology literature. The system performance is better than other published systems, and the implementation is freely available to anyone who is interested in using the system for academic purposes. The system can help researchers quickly discover reported PPIs, and thereby increasing the speed at which databases can be populated and novel signaling pathways can be constructed.

Background: The BioCreative challenge evaluation is a community-wide effort for evaluating text m... more Background: The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested. Results: A User Advisory Group (UAG) actively participated in the IAT design and assessment. The task focused on gene normalization (identifying gene mentions in the article and linking these genes to standard database identifiers), gene ranking based on the overall importance of each gene mentioned in the article, and geneoriented document retrieval (identifying full text papers relevant to a selected gene). Six systems participated and all processed and displayed the same set of articles. The articles were selected based on content known to be problematic for curation, such as ambiguity of gene names, coverage of multiple genes and species, or introduction of a new gene name. Members of the UAG curated three articles for training and assessment purposes, and each member was assigned a system to review. A questionnaire related to the interface usability and task performance (as measured by precision and recall) was answered after systems were used to curate articles. Although the limited number of articles analyzed and users involved in the IAT experiment precluded rigorous quantitative analysis of the results, a qualitative analysis provided valuable insight into some of the problems encountered by users when using the systems. The overall assessment indicates that the system usability features appealed to most users, but the system performance was suboptimal (mainly due to low accuracy in gene normalization). Some of the issues included failure of species identification and gene name ambiguity in the gene normalization task leading to an extensive list of gene identifiers to review, which, in some cases, did not contain the relevant genes. The document retrieval suffered from the same shortfalls. The UAG favored achieving high performance (measured by precision and recall), but strongly recommended the addition of features that facilitate the identification of correct gene and its identifier, such as contextual information to assist in disambiguation. Discussion: The IAT was an informative exercise that advanced the dialog between curators and developers and increased the appreciation of challenges faced by each group. A major conclusion was that the intended users

This report summarizes the participation of the Tsujii-lab group in the 2006 BioCreative2 text mi... more This report summarizes the participation of the Tsujii-lab group in the 2006 BioCreative2 text mining challenge1. It describes the systems used, the results attained, and the lessons learned. The basic idea was to see how well the AKANE system could perform on a full-text Protein- Protein Interaction (PPI) Information Extraction (IE) task. AKANE system is a recently devel- oped, sentence-level PPI system that achieved a 57.3 F-score on the AImed corpus. In order to use the AKANE system for the BioCreative task, the given training data had to be preprocessed. The BioCreative training data contained just a list of interacting protein pair identifiers for each given full-text article, while the expected input for the AKANE system is annotated sentences like in the AImed corpus. In order to transform the full-text articles into AImed sentence-level annotations, the text was first stripped of all HTML coding to get a plain text representation. Then, each mention of protein names were tagged by a Named Entity Recognizer (NER), and all interacting and co-occurring pairs in single sentences were used for training. A pipeline architecture was made to deal with each of these challenges. Some postprocessing was also necessary, in order to trans- form the results from the AKANE system into the expected format for the BioCreative2 challenge. The postprocessing included filtering and ranking the results, and balancing precision and recall to maximize the F-score.

Lecture Notes in Computer Science, 2005

With the increasing amount of biomedical literature, there is a need for automatic extraction of ... more With the increasing amount of biomedical literature, there is a need for automatic extraction of information to support biomedical researchers. Due to incomplete biomedical information databases, the extraction cannot be done straightforward using dictionaries, so several approaches using contextual rules and machine learning have previously been proposed. Our work is inspired by the previous approaches, but is novel in the sense that it combines Google and Gene Ontology for annotating protein interactions. We got promising empirical results -57.5% terms as valid GO annotations, and 16.9% protein names in the answers provided by our system gProt. The total error-rate was 25.6% consisting mainly of overly general answers and syntactic errors, but also including semantic errors, other biological entities (than proteins and GO-terms) and false information sources.

Lecture Notes in Computer Science, 2013

A main activity in meta-design is the creation of design spaces allowing problem owners to act as... more A main activity in meta-design is the creation of design spaces allowing problem owners to act as system developers. Meta-design is a conceptual framework; it does not provide concrete design space solutions or engineering guidelines for constructing tools that support design spaces. This paper discusses the applicability of a model-driven engineering approach for the realization of an end-user service composition framework, in line with the conceptual meta-design framework. We report our experience of using meta-modelling techniques as supported by the Eclipse Modelling Framework (EMF) family of tools. In our work we found that meta-models are well-suited to formalize the composition language, and the core parts of the EMF framework are useful to represent the language elements and user-made compositions both at design and runtime. Although EMF-based tools exist for creating visual editors, we found that in our case these did not map well to the visual notation we selected for our end-users.

Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing - BioNLP '08, 2008

While there are several corpora which claim to have annotations for protein references, the heter... more While there are several corpora which claim to have annotations for protein references, the heterogeneity between the annotations is recognized as an obstacle to develop expensive resources in a synergistic way. Here we present a series of experimental results which show the differences of protein mention annotations made to two corpora, GENIA and AImed.